scikit-learn / scikit-learn/scikit-learn
Ability to cache FeatureUnion transformers
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- Dominant language
- Python
- Stars
- 67.3k
- Forks
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- Avg merge
- 1d 15h
- Merged PRs (30d)
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Description
It seems reasonable to support a memory parameter to FeatureUnion like was recently added to Pipeline (#7990). It is valuable in the sense that parameters in some constituent transformers can be searched over while others are unchanged; those that are unchanged should not need to be re-fit from scratch.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Review the existing Pipeline memory support referenced in #7990 and the FeatureUnion implementation. Determine how a memory parameter should cache unchanged constituent transformers during parameter searches, then verify that repeated fitting avoids refitting those transformers while changed parameters still take effect.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100